Making Mobility as a Service a Reality with Artificial Intelligence

In this special guest feature, Tom Nutley, Business Development Director at Stage Intelligence, discusses how mobile, applications and Artificial Intelligence (AI) are changing what is possible in urban transport by exploring the concept of Mobility as a Service (Maas) and how it can be made a reality. Tom is the Director of Business Development at Stage Intelligence, an Artificial Intelligence company focused on solving complex challenges within the Transport & Logistics industry. Stage enables its partners to rapidly respond to changing demand and accelerate how they make decisions. Tom is responsible for strategic business partnerships, vertical offerings and business development. He has had success in building global relationships with Bike Share Schemes while driving the development of new offerings for autonomous vehicles and Mobility as a Service (MaaS). He is an advocate for Artificial Intelligence in the Transport & Logistics industry and active in helping more businesses benefit from AI.

Mobility as a Service (MaaS) is set to revolutionise urban transport. It brings together multiple transport networks into a single cohesive user experience and enables citizens to use multiple modes of transport to complete their journeys. MaaS removes the complexity from navigating in urban landscapes and methods of getting around into the palm of your hand.

MaaS benefits from the widespread adoption of smart phones as well as new advances in applications and artificial intelligence (AI). Transport networks have existed for decades, and in some cases even hundreds of years, but they have remained largely in silos. Each system has operated independently with limited information sharing.

While this suits the operators and managers of rail systems, bus networks, Bike Share Schemes and other modes of transport, this isn’t the best solution for citizens. Citizens see the need to get from A to B in the fastest, most comfortable and cost efficient way possible. They want to move throughout a city using whatever means are available with as little friction or delays as possible.

MaaS takes the citizens point of view and enables them to access information and routing via all modes of transport available in the city. They gain a tool for travel that meets their app and smartphone-centric expectations and gives them a transportation tool rather than a bunch of disparate options that need to be figured or integrated by the individual.

We are seeing glimpses of this MaaS future in places like Finland where a multimodal transport app was launched in June 2016. Uber has expanded into food delivery but has the potential to extend its platform using its mix of Application Programming Interfaces (APIs), Global Positioning Systems (GPS) and AI. It has already shown that global disruption in transport is possible in only a few years. With the right approach, MaaS can take disruption in urban transport even further.

Making MaaS Work

MaaS is an easy concept to grasp but it can be a challenging one to implement. The immense amounts of data generated by each transport network will need to be managed and made useful in order to create a positive customer experience. This is where AI will play a critical role.

AI will need to be used to ensure that users have access to transportation where and when they need it. Distribution of bikes, taxis, buses and other modes of transport will be key to ensuring that citizens get value from the service and continue to use it. User experience will be paramount to growing MaaS adoption.

AI is necessary to understand all the different things that influence how a city functions and the different ways a transport network can be effected. Things like weather, days of the week, events, changing user behaviors need to be understood to optimise transport. No one day looks the same and AI will need to be employed to take into account these changes and support each transport network with insights and recommendations for fine-tuning and distributing bikes, taxis, buses, trains and even unforeseen modes of transport across a city.

AI will be the glue the holds the MaaS together ensuring that it delivers on its promise of an optimal user experience. For example, Bike Share Schemes cannot function if there are no bikes available where they are needed and no docking stations at the end of journey. Bikes must be distributed according to where they are needed on that particular day. A team of humans processing all of the variables in a city environment could take weeks or months while AI can simplify that process and take into account millions of pieces of data in real-time.

AI will arm MaaS providers with actionable insights that can be acted on to help drive end user satisfaction and usability of their services. Users benefit from a service tailored to their unique requirements, while also using an application that provides smart recommendations for their journey. It enables MaaS to remain focused on citizens and their needs rather than legacy transport networks.

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